A task scheduling method and system based on structured modeling and reinforcement learning, a terminal and a storage medium

Abstract

The application relates to the technical field of data processing, and discloses a task scheduling method and system based on structured modeling and reinforcement learning, a terminal and a storage medium.The method comprises the following steps: acquiring resource state information and task information, topologically connecting the resource state information and the task information to obtain a heterogeneous graph; acquiring an attention weight, performing feature learning on the heterogeneous graph through a graph attention network to obtain an initial representation vector, performing relationship aggregation on the initial representation vector according to the attention weight to obtain a plurality of representation vectors, and performing vector processing on all the representation vectors through the graph attention network to obtain a vectorized representation; and performing task scheduling on the vectorized representation through a scheduling decision model to obtain a scheduling allocation result.The application is based on heterogeneous graph modeling and a graph attention network, combines reinforcement learning, realizes globally optimized task scheduling and resource allocation, and greatly improves the efficiency of task data allocation.

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